https://github.com/justinmarschall/presentations

• My career path
• Data science as fulfilling work
• How to get a job in data science
• Appendix
  • Installing R & RStudio
  • Resources to learn R
  • Publicly available data

• Luther College '12
  • B.A.
  • Major: Psychology
  • Minor: Music
  • Research: Forgiveness and Health
• Iowa State University '15
  • M.S. & Certificate in Quantitative Psychology
  • Major: Psychology
  • Research: Police Interrogation Tactics and False Confessions; Quantitative Methods

• D.M. Services, Inc. (2015 - Current)
  • Customer Satisfaction Data Analyst
  • Data Scientist

• Major interests
  • Working with data
  • Programming
• Skills
  • Research methods
  • Statistics and modeling
  • Design of experiments
  • Diverse research background
  • Communicating to non-technical audience

• Statistics
• Computer Science
• Economics
• Math
• Business
• Psychology
• Physics
• Engineering
• "…or other related quantitative field of study"

• Data wrangling
• Feature engineering
• Statistical modeling
• Report creation
• Make empirical predictions
• Design experiments
• Employee interviewing and onboarding
• Professional development
• Code development
• Auditing

• Help the company make good decisions (with data)

• How likely is it that customer will respond to a marketing campaign?
• Given response, will customer pay their bill?
• Is customer actually who they say they are?
• What products should we market to customer?
• What are the strongest predictors of customer satisfaction?
• What factors contribute to call duration and call silence?
• What is the best way to measure phone agent quality?

• Fast-paced work
• Always working on something new
• Access to data on massive scale
• Large impact on business
• Consult with other DS on their work
• Complex problems require high attention to detail and technical skill
• An engaging field of work that demands lifelong-learning

• Machine learning
• Deep learning
• Optimization
• Automation
• Recommendation engines
• Natural language processing

• Curiosity
• Creativity
• Competency
• Communication

• Coding/BI
  • SQL
  • R
  • Python
  • Shell
  • Excel
  • Git/GitHub
• Statistics
  • Predictive modeling
  • Machine learning

• Take a class/minor/major
• Open source books (see Appendix)
• Codecademy
• Stack Overflow
• Kaggle
• FiveThirtyEight Riddler Puzzles
• Twitter
  • #rstats
  • #TidyTuesday

How many games would we expect to be needed to complete a best-of-seven series if each team has a 50 percent chance of winning each individual game?

How about if one team has a 60 percent chance of winning each game?

How about 70?

Riddle
Answer

riddler <- function(a_pr = 0.5) {
  a_wins <- 0
  b_wins <- 0
  
  while (a_wins < 4 && b_wins < 4) {
    game <- sample(c("A", "B"),
                   size = 1,
                   prob = c(a_pr, 1 - a_pr))
    
    if (game == "A") {
      a_wins <- a_wins + 1
    } else {
      b_wins <- b_wins + 1
    }
    
  }
  
  data.frame(a_pr = a_pr,
             n_games = a_wins + b_wins)
  
}

set.seed(20190411)

riddler()

##   a_pr n_games
## 1  0.5       6

rep(c(0.5, 0.6, 0.7), each = 2)

## [1] 0.5 0.5 0.6 0.6 0.7 0.7

library(tidyverse)

set.seed(20190411)

map_dfr(rep(c(0.5, 0.6, 0.7), each = 10000),
        riddler) %>% 
  head()

##   a_pr n_games
## 1  0.5       6
## 2  0.5       5
## 3  0.5       6
## 4  0.5       6
## 5  0.5       5
## 6  0.5       7

set.seed(20190411)

map_dfr(rep(c(0.5, 0.6, 0.7), each = 10000),
        riddler) %>% 
  group_by(a_pr) %>% 
  summarize(avg_games = mean(n_games),
            n = n())

## # A tibble: 3 x 3
##    a_pr avg_games     n
##   <dbl>     <dbl> <int>
## 1   0.5      5.82 10000
## 2   0.6      5.70 10000
## 3   0.7      5.38 10000

• Creating user functions
• while() loops
• if() logic
• Diverse data structures
• Simulation
• Iteration
• Descriptive statistics
• Auditing code and results
• Easy to extend and visualize

library(plotly)

set.seed(20190411)

# this simulation will take longer to run
map_dfr(rep(seq(0.50, 1.00, 0.01), each = 10000),
        riddler) %>% 
  group_by(a_pr) %>% 
  summarize(avg_games = mean(n_games),
            n = n()) %>% 
  plot_ly(x = ~a_pr, y = ~avg_games, type = "scatter", mode = "lines",
          text = ~avg_games, hoverinfo = "x+text") %>% 
  layout(title = "Expected Number of Games in Best-of-Seven Series 
         \nGiven Varying Win Probability",
         xaxis = list(title = "Team A Win Probability"),
         yaxis = list(title = "Games", range = c(0, 7)),
         hovermode = "compare")

• Internship
• Graduate school
• Complete a research project outside of class
  • Faculty research
  • Web scraping
  • Publicly available data (see Appendix)

To apply or learn more visit www.colonybrands.com

• Talk
  • Friday, April 12 (TOMORROW)
  • 4:00 pm
  • Valders 350
• Overview
  • Getting Started
  • Essential R: 10 Packages for the New and Intermediate UseR
  • Resources for Future Learning

justin.marschall@imsdm.com
jcmarschall
justinmarschall
justinmarschall

https://github.com/justinmarschall/presentations

• R
• RStudio

• R for Data Science
• Advanced R
• The Tidyverse Cookbook
• R Graphics Cookbook
• Learning Statistics with R
• The tidyverse style guide
• Applied Predictive Modeling

• RStudio Cheat Sheets
• RStudio Online Learning
• RStudio Webinars
• Codecademy
• R Vocabulary
• swirl
• Happy Git and GitHub for the useR

• UCI Machine Learning Repository
• Kaggle
• Sports Reference
• Quandl
• data.gov
• FiveThirtyEight
• Built-in data sets in R

data()

• The Data Science Venn Diagram
  • http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
  • Creative Commons Attribution-NonCommercial License
  • © Drew Conway Data Consulting, LLC. 2015
• R for Data Science
  • https://r4ds.had.co.nz/
  • Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
  • Wickham & Grolemund (2017)
• XKCD Machine Learning
  • https://xkcd.com/1838/
  • Creative Commons Attribution-NonCommercial 2.5 License